Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico

Liu, Yuan; Jing, Runyu; Wen, Zhining; Li, Menglong

doi:10.3389/fphar.2019.01489

Cited by 9 publications

(5 citation statements)

References 88 publications

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“…Indeed, at the molecular level, the findings vary depending on the metformin doses and treatment duration, with apparent differences between acute and chronic administration [ 8 ]. Our study was also conducted with supra-pharmacological doses of metformin, but the literature suggests that primary exploration gene expression studies should be conducted with these doses [ 70 ].…”

Section: Discussionmentioning

confidence: 99%

Metformin Treatment Modulates Long Non-Coding RNA Isoforms Expression in Human Cells

Conceição

Dias

Queiroz

et al. 2022

ncRNA

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Long noncoding RNAs (lncRNAs) undergo splicing and have multiple transcribed isoforms. Nevertheless, for lncRNAs, as well as for mRNA, measurements of expression are routinely performed only at the gene level. Metformin is the first-line oral therapy for type 2 diabetes mellitus and other metabolic diseases. However, its mechanism of action remains not thoroughly explained. Transcriptomic analyses using metformin in different cell types reveal that only protein-coding genes are considered. We aimed to characterize lncRNA isoforms that were differentially affected by metformin treatment on multiple human cell types (three cancer, two non-cancer) and to provide insights into the lncRNA regulation by this drug. We selected six series to perform a differential expression (DE) isoform analysis. We also inferred the biological roles for lncRNA DE isoforms using in silico tools. We found the same isoform of an lncRNA (AC016831.6-205) highly expressed in all six metformin series, which has a second exon putatively coding for a peptide with relevance to the drug action. Moreover, the other two lncRNA isoforms (ZBED5-AS1-207 and AC125807.2-201) may also behave as cis-regulatory elements to the expression of transcripts in their vicinity. Our results strongly reinforce the importance of considering DE isoforms of lncRNA for understanding metformin mechanisms at the molecular level.

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Section: Discussionmentioning

confidence: 99%

Metformin Treatment Modulates Long Non-Coding RNA Isoforms Expression in Human Cells

Conceição

Dias

Queiroz

et al. 2022

ncRNA

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“…With the advance of more sophisticated or more physiological in vitro and biomarker methods, more robust IVIVE predictions have been developed in recent years to support applications such as investigating cardiac safety of drugs [ 158 ] and studying the effects of metabolism on drug efficacy [ 159 ]. More omics-oriented PD predictions or enzyme-expression behaviors have been found in recent publications, providing potential substitutions for in vivo tests [ 6 , 160 ].…”

Section: Applications Of Ivive Approachesmentioning

confidence: 99%

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

Chang¹,

Tan

Allen³

et al. 2022

Toxics

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During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

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“…As an IVIVE study in omics data, Liu et al (2020) developed a useful in silico strategy to narrow the data gap between in vitro and in vivo conditions. They modified in vitro data using non-generative matrix factorization methods to improve the correlation with in vivo data, which overcame the shortcomings of previous large-scale genomic data predictions regarding the in vitro - in vivo data gap (Liu et al, 2020). Although non-generative matrix factorization enables macroscopic estimation based on a pattern recognition classifying chemical and biological responses, it does not focus on each gene estimation.…”

Section: Introductionmentioning

confidence: 99%

RAID: Regression Analysis based Inductive DNA microarray for Precise Read-Across

Amano

Yamane

Honda

2022

Preprint

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Chemical structure-based read-across represents a promising method for chemical toxicity evaluation without the need for animal testing; however, a chemical structure is not necessarily related to toxicity. Therefore, in vitro studies were often used for read-across reliability refinement; however, their external validity has been hindered by the gap between in vitro and in vivo conditions. Thus, we developed a virtual DNA microarray, Regression Analysis based Inductive DNA microarray (RAID), which quantitatively predicts in vivo gene expression profiles based on the chemical structure and/or in vitro transcriptome data. For each gene, elastic-net models were constructed using chemical descriptors and in vitro transcriptome data to predict in vivo data from in vitro data (in vitro to in vivo extrapolation; IVIVE). In feature selection, useful genes for assessing the quantitative structure activity relationship (QSAR) and IVIVE were identified. Predicted transcriptome data derived from the RAID system reflected the in vivo gene expression profiles of characteristic hepatotoxic substances. Moreover, gene ontology and pathway analyses indicated that xenobiotic response and metabolic activation via nuclear receptors are related to those gene expressions. The identified IVIVE-related genes were associated with fatty acid-, xenobiotic-, and drug metabolism, indicating that in vitro studies were effective in evaluating these key events. Furthermore, validation studies revealed that chemical substances associated with these key events could be detected as hepatotoxic biosimilar substances. These results indicate that the RAID system could represent an alternative screening test for repeated-dose toxicity test and toxicogenomic analyses. Our technology provides a critical solution to IVIVE-based read-across by considering the mode of action and chemical structures.

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Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico

Cited by 9 publications

References 88 publications

Metformin Treatment Modulates Long Non-Coding RNA Isoforms Expression in Human Cells

Metformin Treatment Modulates Long Non-Coding RNA Isoforms Expression in Human Cells

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

RAID: Regression Analysis based Inductive DNA microarray for Precise Read-Across

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